3D Wind Field Retrieval within Thunderstorm Clouds over Piedmont
Bibliographic record
Abstract
Windstorm, particularly driven by thunderstorms, are among the most destructive natural hazards in Europe causing significant economic losses and causalities. Despite various research, the understanding of thunderstorm outflows and their interaction with built and natural environments remains incomplete, especially in regions prone to intense convective activity, such as the northern Italy. This study focuses on the three-dimensional (3D) structure and dynamics of thunderstorm clouds, emphasizing the formation of downburst and gust fronts that generates damaging surface winds. To construct the 3D wind structure, dual Doppler radar systems are utilized, combining data from operational C-band radar and X-Band radar within the study area. A LiDAR instrument was also operational during the investigated event; however, the scanning LiDAR and C-band radar volume do not overlap due to sheltered positioning of the LiDAR relative to the radar. The inclusion of the X-band radar resolves this issue by covering areas that are blind to C-band radar, thereby re-establishing continuity in measurements across the three instruments. This configuration ensures continuous and comprehensive spatial coverage of wind field measurements, spanning from surface to maximum observation altitude. To carry this out, historical thunderstorm events that occurred in the Piedmont region, Italy, in 2024 are analysed to enhance present understanding of convective dynamics, and the development of severe wind phenomena. This research will also help identify patterns associated with gust fronts and downbursts, hence facilitating improved nowcasting and risk mitigation strategies for these localized windstorms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".